Real-time strategy (RTS) games such as StarCraft II pose multiple interesting challenges for AI-based agents. Rather than tackling full game play, many systems focus on the micromanagement aspects of the game such as moving, targeting, and resource gathering; these tactical decisions only require reasoning over a short-time horizon and are more amenable to machine learning approaches. In contrast, macromanagement addresses strategic gameplay choices such as production decisions and countering opponents' expansion. Ontañón et al. [1]} note that “a good macro player has the larger army” whereas “a good micro player keeps their units alive for a longer amount of time”.
